Multi-scale features. Due to large-scale variations of objects and textures in complex scenes, multi-scale representations are adapted for accurate and effective dense pixel-level prediction in many computer vision tasks. For the MVS task, Feature Pyramid Network (FPN) [1]}, [2]}, [3]}, [4]}, [5]} is a common approach for multi-scale feature extraction. It is designed as the UNet-liked architecture [6]} to fuse features from different scales into a single one. However, the multi-scale of FPN is achieved at an image level through down-upsampling operations. It cannot capture the large-scale variation of objects at a pixel level. Several studies in image segmentation can learn pixel-level multi-scale features by using multiple kernels with different sizes [7]}, [8]}, [9]}, [10]}. However, these methods prefer a large receptive field for segmentation tasks and usually ignore the details of objects essential for MVS tasks. In contrast to the aforementioned methods [2]}, [3]}, [4]}, [5]}, [7]}, our proposed CDSConv can select an optimal scale for each pixel to produce a robust representation and enhance the matching uncertainty. Moreover, our feature extraction CDSFNet can cover denser scales by utilizing the power of a deep CNN.
